Parallel optimization of underwater acoustic models: A survey

Abstract

Underwater acoustic models are effective tools for simulating underwater sound propagation. More than 50 years of research have been conducted on the theory and computational models of sound propagation in the ocean. Unfortunately, underwater sound propagation models were unable to solve practical large-scale three-dimensional problems for many years due to limited computing power and hardware conditions. Since the mid-1980s, research on high performance computing for acoustic propagation models in the field of underwater acoustics has flourished with the emergence of high-performance computing platforms, enabling underwater acoustic propagation models to solve many practical application problems that could not be solved before. In this paper, the contributions of research on high-performance computing for underwater acoustic propagation models since the 1980s are thoroughly reviewed and the possible development directions for the future are outlined.

Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network

“…The earth is abundant in marine resources. With the advancements in human productivity and technology, there is a growing interest in utilizing, developing, and protecting the ocean [ 1 , 2 , 3 , 4 ]. In underwater signal transmission, acoustic signals serve as the primary carriers of information.…”

Three-dimensional acoustic propagation model for shallow waters based on an indirect boundary element method

“…The continuous growth in computational power (hardware equipped with more raw processing power and more memory capacity) opens up the game for methods based on volume or boundary discretization as the finite element method (FEM) and the boundary element method (BEM) (these examples are not exhaustive, see Ref. [14] for a wide review about underwater acoustic models under the high performance computer paradigm). These methods allow for a more realistic modelling since they assume fewer hypotheses than classical and well established ones as the ray methods, PE methods and coupled normal modes, [15] which have been successfully used for several decades.…”